During personalization, a user is represented roughly as follows. Database representations of a person–tabularized collections of logged behaviors in apps or on devices–are converted into feature vectors, permitting the computation of various proximity metrics between pairs of vectors. Next, “neighborhoods” are derived from the set of neighbors in feature space deemed closest to a given user, according to a distance metric. A 10-dimensional feature vector represents a person as an array of 10 numbers, obtained from measurements of observed behavior, thereby replacing the person with a single point in 10-dimensional feature space. To reduce computational costs, the dimensions of the original user-item matrix are often reduced into a lower “latent space” by techniques such as singular value decomposition (SVD). Recently, deep learning approaches to RS have used stacked denoising autoencoders to generate latent representations of users [1]}. Digital representations of persons thus take the form of feature sets of narrow, observed behaviors within an app or device and exclude their unique mental states, social and moral identities and personal narratives.
